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Given the growing global demand for seafood, it is imperative to conduct a comprehensive study on the prevalence and persistence patterns of pathogenic bacteria and viruses associated with specific seafood varieties. This assessment thoroughly examines the safety of seafood products, considering the diverse processing methods employed in the industry. The importance of understanding the behavior of foodborne pathogens, such as Salmonella typhimurium, Vibrio parahaemolyticus, Clostridium botulinum, Listeria monocytogenes, human norovirus, and hepatitis A virus, is emphasized by recent cases of gastroenteritis outbreaks linked to contaminated seafood. This analysis examines outbreaks linked to seafood in the United States and globally, with a particular emphasis on the health concerns posed by pathogenic bacteria and viruses to consumers. Ensuring the safety of seafood is crucial since it directly relates to consumer preferences on sustainability, food safety, provenance, and availability. The review focuses on assessing the frequency, growth, and durability of infections that arise during the processing of seafood. It utilizes next‐generation sequencing to identify the bacteria responsible for these illnesses. Additionally, it analyzes methods for preventing and intervening of infections while also considering the forthcoming challenges in ensuring the microbiological safety of seafood products. This evaluation emphasizes the significance of the seafood processing industry in promptly responding to evolving consumer preferences by offering current information on seafood hazards and future consumption patterns. To ensure the continuous safety and sustainable future of seafood products, it is crucial to identify and address possible threats. [ABSTRACT FROM AUTHOR]

Background: Antibiotic use in food-producing animals can select for antibiotic resistance in bacteria that can be transmitted to people through contamination of food products during meat processing. Contamination resulting in foodborne illness contributes to adverse health outcomes. Some livestock producers have implemented antibiotic use reduction strategies marketed to consumers on regulated retail meat packaging labels ("label claims")., Objective: We investigated whether retail meat label claims were associated with isolation of multidrug-resistant organisms (MDROs, resistant to ≥3 classes of antibiotics) from U.S. meat samples., Methods: We utilized retail meat data from the U.S. Food and Drug Administration National Antimicrobial Resistance Monitoring System (NARMS) collected during 2016-2019 for bacterial contamination of chicken breast, ground turkey, ground beef, and pork chops. We used modified Poisson regression models to compare the prevalence of MDRO contamination among meat samples with any antibiotic restriction label claims versus those without such claims (i.e., conventionally produced)., Results: In NARMS, 62,338 meat samples were evaluated for bacterial growth from 2016-2019. Of these, 24,446 (39%) samples had label claims that indicated antibiotic use was restricted during animal production. MDROs were isolated from 2252 (4%) meat samples, of which 71% (n = 1591) were conventionally produced, and 29% (n = 661) had antibiotic restriction label claims. Compared with conventional samples, meat with antibiotic restriction label claims had a statistically lower prevalence of MDROs (adjusted prevalence ratio: 0.66; 95% CI: 0.61, 0.73). This relationship was consistent for the outcome of any bacterial growth., Impact: This repeated cross-sectional analysis of a nationally representative retail meat surveillance database in the United States supports that retail meats labeled with antibiotic restriction claims were less likely to be contaminated with MDROs compared with retail meat without such claims during 2016-2019. These findings indicate the potential for the public to become exposed to bacterial pathogens via retail meat and emphasizes a possibility that consumers could reduce their exposure to environmental reservoirs of foodborne pathogens that are resistant to antibiotics., Competing Interests: Competing interests: The authors declare no competing interests. Ethical approval: This study used no human or animal subjects. Therefore, ethical approval was not required., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides spectral "fingerprints" information of bacterial cells, was employed to classify serogroups at the cellular level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE) method were conducted to extract principal spectral features for classification task. Based on these features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM) and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in search for the suitable classification models. Among the results, SAE-based classification models performed better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM (94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested the increasing number of training samples have positive effects on classification models. Taking advantage of increasing dataset, the SAE-SR classification model finally performed better than others with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the potential for "Big-Six" identification. Unlabelled Image • Hyperspectral microscopic image data were used for machine learning model development. • Deep learning methods were proposed for classifying non-O157 STEC serogroups. • SAE based classification models performed better than traditional PCA based models. • The effects of data size on the performance of multiple models were discussed. [ABSTRACT FROM AUTHOR]